Macula Densa snRNAseq Quality Control
Jonathan Nelson
14 March 2025
options(future.globals.maxSize = 74 * 1024^3) # 55 GB
getOption("future.globals.maxSize") #59055800320## [1] 794568949761 Load Raw Macula Densa .rds file
1.1 nFeatures, nCounts, and percent.mt
1.2 Identify clusters
1.2.1 Cluster 0: MD1
1.2.2 Cluster 1: MD2
1.2.3 Cluster 2: MD: Activated (cFos)
1.2.4 Cluster 3: EC (Pecam1)
1.2.5 Cluster 4: Stroma (Vim)
1.2.6 Cluster 5: CD (Aqp2)
1.2.7 Cluster 6: IC (Kit)
1.2.8 Cluster 7: PT (Slc34a1)
SO.markers <- FindAllMarkers(SO, only.pos = TRUE)
SO.markers %>%
group_by(cluster) %>%
dplyr::filter(avg_log2FC > 1)SO.markers %>%
group_by(cluster) %>%
dplyr::filter(avg_log2FC > 1) %>%
slice_head(n = 5) %>%
ungroup() -> top10
DoHeatmap(SO, features = top10$gene) + NoLegend()
markers.to.plot1 <- c("Lrp2", # PT
"Slc5a12", # PT-S1
"Slc13a3", # PT-S2
"Slc16a9", # PT-S3
"Havcr1", # Injured PT
"Epha7", # dTL
"Cryab", # dTL
"Cdh13", # dTL1
"Slc14a2", # dTL2
"Slc12a1", # TAL
"Umod", # TAL, DCT1
"Egf", # TAL, DCT1,
"Cldn10", # TAL
"Cldn16", # TAL
"Nos1", # MD
"Slc12a3", # DCT
"Pvalb", # DCT1
"Slc8a1", # DCT2, CNT
"Aqp2", # PC
"Slc4a1", # IC-A
"Slc26a4", # IC-B
"Nphs1", # Podo
"Ncam1", # PEC
"Flt1", # Endo
"Emcn", # Glom Endo
"Kdr", # Capillary Endo
"Pdgfrb", # Perivascular
"Pdgfra", # Fib
"Piezo2", # Mesangial
"Acta2", # Mural
"Ptprc", # Immune
"Cd74", # Macrophage
"Skap1", # B/T Cells
"Upk1b", # Uro
"Top2a" # Proliferation
)
DotPlot(SO,
features = markers.to.plot1,
dot.scale = 8,
dot.min = 0,
scale.max = 100,
scale.min = 0,
col.min = -2.5,
col.max = 2.5)+
coord_flip()
2 Filter non-MD Cells and re-cluster
SO2 <- SCTransform(SO2) %>%
RunPCA() %>%
FindNeighbors(dims = 1:30) %>%
FindClusters() %>%
RunUMAP(dims = 1:30)## | | | 0% | |================== | 25% | |=================================== | 50% | |==================================================== | 75% | |======================================================================| 100%## | | | 0% | |== | 2% | |=== | 4% | |===== | 7% | |====== | 9% | |======== | 11% | |========= | 13% | |=========== | 15% | |============ | 17% | |============== | 20% | |=============== | 22% | |================= | 24% | |================== | 26% | |==================== | 28% | |===================== | 30% | |======================= | 33% | |======================== | 35% | |========================== | 37% | |=========================== | 39% | |============================= | 41% | |============================== | 43% | |================================ | 46% | |================================= | 48% | |=================================== | 50% | |===================================== | 52% | |====================================== | 54% | |======================================== | 57% | |========================================= | 59% | |=========================================== | 61% | |============================================ | 63% | |============================================== | 65% | |=============================================== | 67% | |================================================= | 70% | |================================================== | 72% | |==================================================== | 74% | |===================================================== | 76% | |======================================================= | 78% | |======================================================== | 80% | |========================================================== | 83% | |=========================================================== | 85% | |============================================================= | 87% | |============================================================== | 89% | |================================================================ | 91% | |================================================================= | 93% | |=================================================================== | 96% | |==================================================================== | 98% | |======================================================================| 100%## | | | 0% | |== | 2% | |=== | 4% | |===== | 7% | |====== | 9% | |======== | 11% | |========= | 13% | |=========== | 15% | |============ | 17% | |============== | 20% | |=============== | 22% | |================= | 24% | |================== | 26% | |==================== | 28% | |===================== | 30% | |======================= | 33% | |======================== | 35% | |========================== | 37% | |=========================== | 39% | |============================= | 41% | |============================== | 43% | |================================ | 46% | |================================= | 48% | |=================================== | 50% | |===================================== | 52% | |====================================== | 54% | |======================================== | 57% | |========================================= | 59% | |=========================================== | 61% | |============================================ | 63% | |============================================== | 65% | |=============================================== | 67% | |================================================= | 70% | |================================================== | 72% | |==================================================== | 74% | |===================================================== | 76% | |======================================================= | 78% | |======================================================== | 80% | |========================================================== | 83% | |=========================================================== | 85% | |============================================================= | 87% | |============================================================== | 89% | |================================================================ | 91% | |================================================================= | 93% | |=================================================================== | 96% | |==================================================================== | 98% | |======================================================================| 100%## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
##
## Number of nodes: 11529
## Number of edges: 391481
##
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.7904
## Number of communities: 15
## Elapsed time: 1 seconds
markers.to.plot2 <- c("Lrp2", # PT
"Slc5a12", # PT-S1
"Slc13a3", # PT-S2
"Slc16a9", # PT-S3
"Havcr1", # Injured PT
"Epha7", # dTL
"Cryab", # dTL
"Cdh13", # dTL1
"Slc14a2", # dTL2
"Slc12a1", # TAL
"Umod", # TAL, DCT1
"Egf", # TAL, DCT1,
"Cldn10", # TAL
"Cldn16", # TAL
"Nos1", # MD
"Slc12a3", # DCT
"Pvalb", # DCT1
"Slc8a1", # DCT2, CNT
"Aqp2", # PC
"Slc4a1", # IC-A
"Slc26a4", # IC-B
"Nphs1", # Podo
"Ncam1", # PEC
"Flt1", # Endo
"Emcn", # Glom Endo
"Kdr", # Capillary Endo
"Pdgfrb", # Perivascular
"Pdgfra", # Fib
"Piezo2", # Mesangial
"Acta2", # Mural
"Ptprc", # Immune
"Cd74", # Macrophage
"Skap1", # B/T Cells
"Upk1b", # Uro
"Top2a", # Proliferation
"Cldn5",
"Jun",
"Fosb"
)
DotPlot(SO2,
features = markers.to.plot2,
dot.scale = 8,
dot.min = 0,
scale.max = 100,
scale.min = 0,
col.min = -2.5,
col.max = 2.5)+
coord_flip()
3 Subset and recluster
SO3 <- SCTransform(SO3) %>%
RunPCA() %>%
FindNeighbors(dims = 1:10) %>%
FindClusters(resolution = .3) %>%
RunUMAP(dims = 1:10)## | | | 0% | |================== | 25% | |=================================== | 50% | |==================================================== | 75% | |======================================================================| 100%## | | | 0% | |== | 2% | |=== | 4% | |===== | 7% | |====== | 9% | |======== | 11% | |========= | 13% | |=========== | 15% | |============ | 17% | |============== | 20% | |=============== | 22% | |================= | 24% | |================== | 26% | |==================== | 28% | |===================== | 30% | |======================= | 33% | |======================== | 35% | |========================== | 37% | |=========================== | 39% | |============================= | 41% | |============================== | 43% | |================================ | 46% | |================================= | 48% | |=================================== | 50% | |===================================== | 52% | |====================================== | 54% | |======================================== | 57% | |========================================= | 59% | |=========================================== | 61% | |============================================ | 63% | |============================================== | 65% | |=============================================== | 67% | |================================================= | 70% | |================================================== | 72% | |==================================================== | 74% | |===================================================== | 76% | |======================================================= | 78% | |======================================================== | 80% | |========================================================== | 83% | |=========================================================== | 85% | |============================================================= | 87% | |============================================================== | 89% | |================================================================ | 91% | |================================================================= | 93% | |=================================================================== | 96% | |==================================================================== | 98% | |======================================================================| 100%## | | | 0% | |== | 2% | |=== | 4% | |===== | 7% | |====== | 9% | |======== | 11% | |========= | 13% | |=========== | 15% | |============ | 17% | |============== | 20% | |=============== | 22% | |================= | 24% | |================== | 26% | |==================== | 28% | |===================== | 30% | |======================= | 33% | |======================== | 35% | |========================== | 37% | |=========================== | 39% | |============================= | 41% | |============================== | 43% | |================================ | 46% | |================================= | 48% | |=================================== | 50% | |===================================== | 52% | |====================================== | 54% | |======================================== | 57% | |========================================= | 59% | |=========================================== | 61% | |============================================ | 63% | |============================================== | 65% | |=============================================== | 67% | |================================================= | 70% | |================================================== | 72% | |==================================================== | 74% | |===================================================== | 76% | |======================================================= | 78% | |======================================================== | 80% | |========================================================== | 83% | |=========================================================== | 85% | |============================================================= | 87% | |============================================================== | 89% | |================================================================ | 91% | |================================================================= | 93% | |=================================================================== | 96% | |==================================================================== | 98% | |======================================================================| 100%## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
##
## Number of nodes: 11471
## Number of edges: 355404
##
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.8631
## Number of communities: 6
## Elapsed time: 1 seconds
SO.markers <- FindAllMarkers(SO3, only.pos = TRUE)
SO.markers %>%
group_by(cluster) %>%
dplyr::filter(avg_log2FC > .5) %>%
slice_head(n = 10) %>%
ungroup() -> top10
DoHeatmap(SO3, features = top10$gene) + NoLegend()
# Session Info
## R version 4.3.1 (2023-06-16 ucrt)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 11 x64 (build 22631)
##
## Matrix products: default
##
##
## locale:
## [1] LC_COLLATE=English_United States.utf8
## [2] LC_CTYPE=English_United States.utf8
## [3] LC_MONETARY=English_United States.utf8
## [4] LC_NUMERIC=C
## [5] LC_TIME=English_United States.utf8
##
## time zone: America/Los_Angeles
## tzcode source: internal
##
## attached base packages:
## [1] grid stats4 stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] CellChat_2.1.2 igraph_2.1.4
## [3] ggtext_0.1.2 ComplexHeatmap_2.16.0
## [5] UpSetR_1.4.0 EnhancedVolcano_1.18.0
## [7] lubridate_1.9.4 forcats_1.0.0
## [9] purrr_1.0.4 readr_2.1.5
## [11] tidyr_1.3.1 tidyverse_2.0.0
## [13] enrichplot_1.20.3 clusterProfiler_4.8.3
## [15] gplots_3.2.0 kableExtra_1.4.0
## [17] ggvenn_0.1.10 data.table_1.17.0
## [19] readxl_1.4.3 openxlsx_4.2.8
## [21] car_3.1-3 carData_3.0-5
## [23] stringr_1.5.1 here_1.0.1
## [25] DESeq2_1.40.2 SummarizedExperiment_1.30.2
## [27] Biobase_2.60.0 MatrixGenerics_1.12.3
## [29] matrixStats_1.5.0 GenomicRanges_1.52.1
## [31] GenomeInfoDb_1.36.4 IRanges_2.34.1
## [33] S4Vectors_0.38.2 BiocGenerics_0.46.0
## [35] ggrepel_0.9.6 RColorBrewer_1.1-3
## [37] ggpmisc_0.6.1 ggpp_0.5.8-1
## [39] tibble_3.2.1 BiocManager_1.30.25
## [41] ggplot2_3.5.1 knitr_1.49
## [43] patchwork_1.3.0 SeuratObject_5.0.2
## [45] Seurat_4.4.0 dplyr_1.1.4
##
## loaded via a namespace (and not attached):
## [1] goftest_1.2-3 Biostrings_2.68.1 vctrs_0.6.5
## [4] spatstat.random_3.3-2 digest_0.6.37 png_0.1-8
## [7] shape_1.4.6.1 registry_0.5-1 deldir_2.0-4
## [10] parallelly_1.42.0 MASS_7.3-60 reshape2_1.4.4
## [13] httpuv_1.6.15 foreach_1.5.2 qvalue_2.32.0
## [16] withr_3.0.2 ggrastr_1.0.2 xfun_0.51
## [19] ggfun_0.1.8 ggpubr_0.6.0 survival_3.5-5
## [22] memoise_2.0.1 ggbeeswarm_0.7.2 MatrixModels_0.5-3
## [25] gson_0.1.0 systemfonts_1.2.1 tidytree_0.4.6
## [28] zoo_1.8-13 GlobalOptions_0.1.2 gtools_3.9.5
## [31] pbapply_1.7-2 Formula_1.2-5 KEGGREST_1.40.1
## [34] promises_1.3.2 httr_1.4.7 downloader_0.4
## [37] rstatix_0.7.2 globals_0.16.3 fitdistrplus_1.2-2
## [40] rstudioapi_0.17.1 miniUI_0.1.1.1 generics_0.1.3
## [43] DOSE_3.26.2 ggalluvial_0.12.5 zlibbioc_1.46.0
## [46] ggraph_2.2.1 polyclip_1.10-7 GenomeInfoDbData_1.2.10
## [49] xtable_1.8-4 doParallel_1.0.17 evaluate_1.0.3
## [52] S4Arrays_1.2.0 hms_1.1.3 irlba_2.3.5.1
## [55] colorspace_2.1-1 polynom_1.4-1 ggnetwork_0.5.13
## [58] ROCR_1.0-11 reticulate_1.41.0 spatstat.data_3.1-4
## [61] magrittr_2.0.3 lmtest_0.9-40 later_1.4.1
## [64] viridis_0.6.5 ggtree_3.8.2 lattice_0.21-8
## [67] spatstat.geom_3.3-5 NMF_0.28 future.apply_1.11.3
## [70] SparseM_1.84-2 scattermore_1.2 shadowtext_0.1.4
## [73] cowplot_1.1.3 RcppAnnoy_0.0.22 pillar_1.10.1
## [76] nlme_3.1-162 sna_2.8 iterators_1.0.14
## [79] gridBase_0.4-7 caTools_1.18.3 compiler_4.3.1
## [82] RSpectra_0.16-2 stringi_1.8.4 tensor_1.5
## [85] plyr_1.8.9 crayon_1.5.3 abind_1.4-8
## [88] gridGraphics_0.5-1 locfit_1.5-9.8 sp_2.2-0
## [91] graphlayouts_1.2.2 bit_4.5.0.1 fastmatch_1.1-6
## [94] codetools_0.2-20 bslib_0.9.0 GetoptLong_1.0.5
## [97] plotly_4.10.4 mime_0.12 splines_4.3.1
## [100] circlize_0.4.16 Rcpp_1.0.14 quantreg_6.00
## [103] HDO.db_0.99.1 cellranger_1.1.0 gridtext_0.1.5
## [106] blob_1.2.4 clue_0.3-66 fs_1.6.5
## [109] listenv_0.9.1 ggsignif_0.6.4 ggplotify_0.1.2
## [112] Matrix_1.6-5 tzdb_0.4.0 svglite_2.1.3
## [115] network_1.19.0 tweenr_2.0.3 pkgconfig_2.0.3
## [118] tools_4.3.1 cachem_1.1.0 RSQLite_2.3.9
## [121] viridisLite_0.4.2 DBI_1.2.3 fastmap_1.2.0
## [124] rmarkdown_2.29 scales_1.3.0 ica_1.0-3
## [127] broom_1.0.7 sass_0.4.9 coda_0.19-4.1
## [130] FNN_1.1.4.1 dotCall64_1.2 RANN_2.6.2
## [133] farver_2.1.2 tidygraph_1.3.1 scatterpie_0.2.4
## [136] yaml_2.3.10 cli_3.6.4 leiden_0.4.3.1
## [139] lifecycle_1.0.4 uwot_0.2.3 presto_1.0.0
## [142] backports_1.5.0 BiocParallel_1.34.2 timechange_0.3.0
## [145] gtable_0.3.6 rjson_0.2.23 ggridges_0.5.6
## [148] progressr_0.15.1 limma_3.56.2 parallel_4.3.1
## [151] ape_5.8-1 jsonlite_1.9.0 bitops_1.0-9
## [154] bit64_4.6.0-1 Rtsne_0.17 yulab.utils_0.2.0
## [157] spatstat.utils_3.1-2 BiocNeighbors_1.18.0 zip_2.3.2
## [160] jquerylib_0.1.4 GOSemSim_2.26.1 spatstat.univar_3.1-1
## [163] lazyeval_0.2.2 shiny_1.10.0 htmltools_0.5.8.1
## [166] GO.db_3.17.0 sctransform_0.4.1 glue_1.8.0
## [169] spam_2.11-1 XVector_0.40.0 RCurl_1.98-1.12
## [172] rprojroot_2.0.4 treeio_1.24.3 gridExtra_2.3
## [175] R6_2.6.1 labeling_0.4.3 cluster_2.1.4
## [178] rngtools_1.5.2 aplot_0.2.5 statnet.common_4.11.0
## [181] vipor_0.4.7 DelayedArray_0.26.7 tidyselect_1.2.1
## [184] ggforce_0.4.2 xml2_1.3.7 AnnotationDbi_1.62.2
## [187] future_1.34.0 munsell_0.5.1 KernSmooth_2.23-21
## [190] htmlwidgets_1.6.4 fgsea_1.26.0 rlang_1.1.5
## [193] spatstat.sparse_3.1-0 spatstat.explore_3.3-4 beeswarm_0.4.0